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ruv
9ad550d95f
Candle native port — wifi-densepose-occworld-candle v0.3.0: - config.rs: OccWorldConfig (14 params matching occworld.py) - vqvae.rs: ClassEmbedding(18→64), VQCodebook(512×512, squared-L2), QuantConv/PostQuantConv(1×1 Conv2d), fold_3d_to_2d helpers ResNet encoder/decoder are documented stubs (Phase 5 checkpoint pending) - transformer.rs: full Candle MHA transformer (2 layers, temporal+spatial cross-attention, FFN, pre-norm residuals) - inference.rs: OccWorldCandle::dummy() + ::load() + predict() InferenceOutput: sem_pred(1,15,200,200,16) + trajectory_priors - 14/14 tests pass (12 lib + 2 doctests) GCP GPU scripts — scripts/gcp/: - provision_training.sh: a2-highgpu-8g (8×A100 40GB) for Phase 5 retraining - run_training.sh: rsync + torchrun 8-GPU train + checkpoint download - provision_cosmos.sh: a2-ultragpu-1g (A100 80GB) for Cosmos evaluation - cosmos_eval.sh: run Cosmos-Transfer2.5 inference, download results - teardown.sh: safe checkpoint download + instance delete Co-Authored-By: claude-flow <ruv@ruv.net>
31 lines
880 B
TOML
31 lines
880 B
TOML
[package]
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name = "wifi-densepose-occworld-candle"
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description = "ADR-147 — OccWorld TransVQVAE inference ported to Candle (Rust-native, no Python IPC)"
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version.workspace = true
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edition.workspace = true
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authors.workspace = true
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license.workspace = true
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repository.workspace = true
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[dependencies]
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# Candle ML framework — pin to 0.9 (same as cog-person-count).
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# The `cuda` feature is opt-in; CPU is the default.
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candle-core = { version = "0.9", default-features = false }
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candle-nn = { version = "0.9", default-features = false }
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serde = { workspace = true, features = ["derive"] }
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serde_json.workspace = true
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thiserror.workspace = true
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tokio = { version = "1", features = ["fs", "macros"] }
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safetensors = "0.4"
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[dev-dependencies]
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approx = "0.5"
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[features]
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default = []
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cuda = ["candle-core/cuda", "candle-nn/cuda"]
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[lints.rust]
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unsafe_code = "forbid"
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missing_docs = "warn"
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